The problem of blind identification of channel codes at a receiver involves identifying a code chosen by a transmitter from a known code-family, by observing the transmitted codewords through the channel. Most existing approaches for code-identification are contingent upon the codes in the family having some special structure, and are often computationally expensive otherwise. Further, rigorous analytical guarantees on the performance of these existing techniques are largely absent. This work presents a new method for code-identification on the binary symmetric channel (BSC), inspired by the framework of subspace codes for operator channels, carefully combining principles of hamming-metric and subspace-metric decoding. We refer to this method as the minimum denoised subspace discrepancy decoder. We present theoretical guarantees for code-identification using this decoder, for bounded-weight errors, and also present a bound on the probability of error when used on the BSC. Simulations demonstrate the improved performance of our decoder for random linear codes beyond existing general-purpose techniques, across most channel conditions and even with a limited number of received vectors.

翻译：信道码盲识别问题涉及接收机通过信道观察传输的码字，从已知码族中识别出发射机所选用的编码。现有的大多数码识别方法依赖于码族中编码具有特殊结构，否则通常计算成本高昂。此外，这些现有技术的性能缺乏严格的分析保证。本文提出了一种针对二进制对称信道（BSC）的码识别新方法，该方法受用于算子信道的子空间编码框架启发，巧妙结合了汉明度量和子空间度量译码原理。我们将此方法称为最小去噪子空间差异译码器。我们为该译码器在有界权重错误下的码识别提供了理论保证，并给出了在BSC上使用时的误码概率界。仿真结果表明，在大多数信道条件下，即使接收向量数量有限，我们的译码器对随机线性码的性能也优于现有的通用技术。